The present invention relates to the estimation and reporting of overall air quality inside a home, and in particular to the inference of air quality based on a combination of temporally indexed air quality values, additional environmental and behavioral home context and computational analytics.
Significant bodies of research indicate that cumulative, personal exposure to fine particulates (i.e. PM2.5) is strongly correlated with pulmonary disease and cardiovascular disease. PM2.5 is defined as the aggregate measure, by weight, of all fine and ultrafine particulate pollution in the air, with particle sizes 2.5 microns and below. PM2.5 is typically measured as micrograms per cubic meter. PM2.5 is measured in a federal regulatory manner by collecting 2.5 micron and smaller particles in a filter, then measuring the change in weight of the filter paper. Additionally, particles are counted, typically in particles per liter, by measuring the scattering of collimated light in a dark chamber off individual particles.
In addition, statistically significant correlations have now been discovered between exposure to PM2.5 by pregnant women and the onset of autism and attention deficit hyperactivity disorder in children. The residential home represents a large portion of a person's overall exposure profile to PM2.5, and therefore direct measurement and reporting of home air pollution can provide valuable insight into mitigation of overall fine particular exposure in order to maximize long-term and short-term health. The existing state of the art in fine particulate measurement provides instantaneous readings in particle counts per volume or in particle mass per unit volume. Numerous devices provide such information, for instance using light-based scattering and using impactors that embed desired particle sizes on a substrate suitable for direct optical measurement. Such instantaneous readings are dominated by the influence of human activity in the home, and therefore the state of the art fails to provide authentic measures of the home's true air quality as a system value. Other instances in the state of the art avoid direct reporting of instantaneous values, instead providing either direct feedback-based control of air handling units or providing direct ventilation recommendations to the resident. Neither of these types of solutions presents residents with interactive, spatiotemporally explorable data regarding air quality values in order to empower the resident to employ experimentation, observation and reflection to improve indoor air quality over the long term. Furthermore the state of the art fails to provide such insight in the context of comparing indoor particulate values analytically with nearby, outdoor particulate values to ascertain the effectiveness of home air pollution remediation techniques. Existing techniques use indoor and outdoor measured values to generate ventilation control commands and recommendations but fail to present indoor/outdoor air pollution differentials directly to the resident in spatiotemporally explorable formats in order to provide insight regarding the home's air quality health as compared to ambient pollution state. Furthermore the state of the art fails to perform temporal and spatial trending analysis, comparing the current air pollution levels inside the home to past values over multiple temporal resolutions, nor comparing the current and past air pollution levels of the home to other homes in the spatial vicinity. Finally, existing air pollution monitoring techniques fail to provide interfaces and analytical methods affording the user the ability to annotate behavioral context (e.g. the purchase and installation of air purifiers in the home; the cleaning of HVAC systems) and then assess the effectiveness of such interventions over short- and long-term temporal spans.